Precision Agriculture: Temporal and Spatial Modeling of Wheat Canopy Spectral Characteristics
文献类型： 外文期刊
作者： Zhang, Donghui 1 ; Hou, Liang 2 ; Lv, Liangjie 3 ; Qi, Hao 2 ; Sun, Haifang 2 ; Zhang, Xinshi 2 ; Li, Si 2 ; Min, Jianan 2 ; Liu, Yanwen 4 ; Tang, Yuanyuan 5 ; Liao, Yao 6 ;
作者机构： 1.China Acad Space Technol, Inst Remote Sensing Satellite, Beijing 100094, Peoples R China
2.Hebei Acad Agr & Forestry Sci, Inst Agr Informat & Econ, Shijiazhuang 050051, Peoples R China
3.Hebei Acad Agr & Forestry Sci, Inst Cereal & Oil Crops, Shijiazhuang 050035, Peoples R China
4.Hubei Univ Sci & Technol, Sch Resources Environm Sci & Engn, Xianning 437100, Peoples R China
5.China Geol Survey, Changsha Nat Resources Comprehens Survey Ctr, Changsha 410600, Peoples R China
6.Guizhou Ecol Meteorol & Agrometeorol Ctr, Guiyang 550002, Peoples R China
关键词： UAV remote sensing; wheat growth stages; multispectral analysis; spatio-temporal monitoring; precision agriculture
期刊名称：AGRICULTURE-BASEL （ 影响因子：3.6； 五年影响因子：3.8 ）
ISSN：
年卷期： 2025 年 15 卷 3 期
页码：
收录情况： SCI
摘要： This study investigates the dynamic changes in wheat canopy spectral characteristics across seven critical growth stages (Tillering, Pre-Jointing, Jointing, Post-Jointing, Booting, Flowering, and Ripening) using UAV-based multispectral remote sensing. By analyzing four key spectral bands-green (G), red (R), red-edge (RE), and near-infrared (NIR)-and their combinations, we identify spectral features that reflect changes in canopy activity, health, and structure. Results show that the green band is highly sensitive to chlorophyll activity and low canopy coverage during the Tillering stage, while the NIR band captures structural complexity and canopy density during the Jointing and Booting stages. The combination of G and NIR bands reveals increased canopy density and spectral concentration during the Booting stage, while the RE band effectively detects plant senescence and reduced spectral uniformity during the ripening stage. Time-series analysis of spectral data across growth stages improves the accuracy of growth stage identification, with dynamic spectral changes offering insights into growth inflection points. Spatially, the study demonstrates the potential for identifying field-level anomalies, such as water stress or disease, providing actionable data for targeted interventions. This comprehensive spatio-temporal monitoring framework improves crop management and offers a cost-effective, precise solution for disease prediction, yield forecasting, and resource optimization. The study paves the way for integrating UAV remote sensing into precision agriculture practices, with future research focusing on hyperspectral data integration to enhance monitoring models.
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